Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "240" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 17 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 17 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459994 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.330401 | -0.157492 | 0.065601 | -0.518934 | -0.579266 | -0.947539 | 7.737826 | 4.472321 | 0.5923 | 0.6110 | 0.3782 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.533212 | 0.060948 | 0.169122 | -0.303871 | -0.156404 | -1.117310 | 8.944671 | 4.509626 | 0.5853 | 0.5994 | 0.3902 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.665843 | 0.331542 | -0.341920 | -0.761599 | -0.658535 | -1.191056 | 10.072422 | 5.746941 | 0.5861 | 0.6040 | 0.3879 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.462128 | 0.360885 | -0.109181 | -0.730675 | -0.746399 | -1.378384 | 8.837468 | 5.110513 | 0.5860 | 0.6083 | 0.3864 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.904655 | 0.238565 | -0.403455 | -0.804222 | -0.787850 | -1.591560 | 11.168979 | 5.892256 | 0.5871 | 0.6108 | 0.3793 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.229031 | -0.202169 | 0.002801 | -0.502324 | -0.530965 | -1.075570 | 10.814967 | 5.087493 | 0.5884 | 0.6101 | 0.3800 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.069576 | 0.094321 | -0.542810 | -0.928643 | -0.634618 | -1.263400 | 6.445808 | 1.836637 | 0.6108 | 0.6361 | 0.3445 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.839655 | -0.169514 | -0.610936 | -1.048795 | -0.582231 | -1.573679 | 11.185773 | 6.335391 | 0.5938 | 0.6151 | 0.3836 | nan | nan |
| 2459984 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.474974 | -0.225048 | -0.035115 | -0.423656 | -0.221248 | -1.597118 | 1.993057 | 0.727779 | 0.6097 | 0.6328 | 0.3625 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.665979 | 0.092427 | -0.721240 | -0.831313 | -0.631795 | -1.451039 | 1.965488 | 1.772339 | 0.6194 | 0.6465 | 0.3336 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.344737 | 0.846050 | -1.070823 | -1.198748 | -1.063580 | -0.980486 | -0.817046 | -1.012080 | 0.6805 | 0.6807 | 0.2925 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.495082 | 0.436913 | -0.720166 | -0.702150 | 5.737918 | 8.786522 | 3.918949 | 5.734632 | 0.5974 | 0.6167 | 0.3819 | nan | nan |
| 2459980 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.076244 | -0.038081 | 0.739985 | 1.085737 | -0.352123 | 0.227736 | 0.411650 | 0.899245 | 0.6508 | 0.6594 | 0.3170 | nan | nan |
| 2459979 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.559553 | 0.627850 | 1.279648 | 1.505788 | 0.363643 | 0.841883 | 2.988181 | 2.930400 | 0.6026 | 0.6222 | 0.3883 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.796305 | 0.237179 | 1.044992 | 1.409515 | -0.183940 | 0.524419 | 5.363552 | 5.925902 | 0.6039 | 0.6226 | 0.3961 | nan | nan |
| 2459977 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.645131 | 0.438668 | 0.811296 | 1.105186 | 0.193257 | 0.546299 | 1.566277 | 2.469695 | 0.5667 | 0.5841 | 0.3541 | nan | nan |
| 2459976 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.872155 | 0.366688 | 0.913017 | 1.333717 | -0.105278 | 0.805266 | 3.075731 | 3.776663 | 0.6116 | 0.6287 | 0.3853 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 7.737826 | -0.330401 | -0.157492 | 0.065601 | -0.518934 | -0.579266 | -0.947539 | 7.737826 | 4.472321 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 8.944671 | 0.533212 | 0.060948 | 0.169122 | -0.303871 | -0.156404 | -1.117310 | 8.944671 | 4.509626 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 10.072422 | 0.331542 | 0.665843 | -0.761599 | -0.341920 | -1.191056 | -0.658535 | 5.746941 | 10.072422 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 8.837468 | 0.360885 | 0.462128 | -0.730675 | -0.109181 | -1.378384 | -0.746399 | 5.110513 | 8.837468 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 11.168979 | 0.238565 | 0.904655 | -0.804222 | -0.403455 | -1.591560 | -0.787850 | 5.892256 | 11.168979 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 10.814967 | 0.229031 | -0.202169 | 0.002801 | -0.502324 | -0.530965 | -1.075570 | 10.814967 | 5.087493 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 6.445808 | 0.094321 | 1.069576 | -0.928643 | -0.542810 | -1.263400 | -0.634618 | 1.836637 | 6.445808 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 11.185773 | -0.169514 | 0.839655 | -1.048795 | -0.610936 | -1.573679 | -0.582231 | 6.335391 | 11.185773 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 1.993057 | 0.474974 | -0.225048 | -0.035115 | -0.423656 | -0.221248 | -1.597118 | 1.993057 | 0.727779 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 1.965488 | -0.665979 | 0.092427 | -0.721240 | -0.831313 | -0.631795 | -1.451039 | 1.965488 | 1.772339 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | nn Shape | 0.846050 | 0.344737 | 0.846050 | -1.070823 | -1.198748 | -1.063580 | -0.980486 | -0.817046 | -1.012080 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | nn Temporal Variability | 8.786522 | 0.436913 | -0.495082 | -0.702150 | -0.720166 | 8.786522 | 5.737918 | 5.734632 | 3.918949 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | nn Power | 1.085737 | -0.038081 | -1.076244 | 1.085737 | 0.739985 | 0.227736 | -0.352123 | 0.899245 | 0.411650 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | ee Temporal Discontinuties | 2.988181 | -0.559553 | 0.627850 | 1.279648 | 1.505788 | 0.363643 | 0.841883 | 2.988181 | 2.930400 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | nn Temporal Discontinuties | 5.925902 | 0.237179 | -0.796305 | 1.409515 | 1.044992 | 0.524419 | -0.183940 | 5.925902 | 5.363552 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | nn Temporal Discontinuties | 2.469695 | -0.645131 | 0.438668 | 0.811296 | 1.105186 | 0.193257 | 0.546299 | 1.566277 | 2.469695 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 240 | N19 | RF_maintenance | nn Temporal Discontinuties | 3.776663 | 0.366688 | -0.872155 | 1.333717 | 0.913017 | 0.805266 | -0.105278 | 3.776663 | 3.075731 |